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		<h1>Publications</h1>
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					<a href="{{ STATIC_URL }}siggraph2013/opensurfaces.pdf">
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						<h3>OpenSurfaces: A Richly Annotated Catalog of Surface Appearance</h3>
						<p><a href="http://www.cs.cornell.edu/~sbell/" target="_blank">Sean Bell</a>, <a href="http://www.cs.cornell.edu/~paulu/">Paul Upchurch</a>, <a href="http://www.cs.cornell.edu/~snavely/" target="_blank">Noah Snavely</a>, <a href="http://www.cs.cornell.edu/~kb/" target="_blank">Kavita Bala</a>
						<br/><a href="http://rgb.cs.cornell.edu" target="_blank">Cornell University</a></p>
						<p><i>ACM Transactions on Graphics (SIGGRAPH 2013)</i></p>
						<p>
							<a class="btn" href="http://labelmaterial.s3.amazonaws.com/release/siggraph2013-opensurfaces.pdf">Paper (49MB PDF)</a>&nbsp;
							<a class="btn" href="http://labelmaterial.s3.amazonaws.com/release/siggraph2013-supplemental.pdf">Supplemental (13MB PDF)</a>&nbsp;
							<a class="btn" href="http://labelmaterial.s3.amazonaws.com/release/siggraph2013-talk-final.key.zip">Slides (273M Keynote '09)</a>&nbsp;
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		<p><i>Abstract:</i></p>
		<p>The appearance of surfaces in real-world scenes is determined by the
		materials, textures, and context in which the surfaces appear. However,
		the datasets we have for visualizing and modeling rich surface
		appearance in context, in applications such as home remodeling, are
		quite limited. To help address this need, we present OpenSurfaces, a
		rich, labeled database consisting of thousands of examples of surfaces
		segmented from consumer photographs of interiors, and annotated with
		material parameters (reﬂectance, material names), texture information
		(surface normals, rectiﬁed textures), and contextual information (scene
		category, and object names).</p>

		<p>Retrieving usable surface information from uncalibrated Internet
		photo collections is challenging. We use human annotations and present
		a new methodology for segmenting and annotating materials in Internet
		photo collections suitable for crowdsourcing (e.g., through Amazon’s
		Mechanical Turk). Because of the noise and variability inherent in
		Internet photos and novice annotators, designing this annotation engine
		was a key challenge; we present a multi-stage set of annotation tasks
		with quality checks and validation. We demonstrate the use of this
		database in proof-of-concept applications including surface retexturing
		and material and image browsing, and discuss future uses. OpenSurfaces
		is a public resource available at <a href="http://opensurfaces.cs.cornell.edu/">http://opensurfaces.cs.cornell.edu/</a>.</p>
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		<p><i>Video:</i></p>
		<div class="thumbnail" style="height:315px">
			<iframe src="http://player.vimeo.com/video/64893531?title=0&amp;byline=0&amp;portrait=0" width="100%" height="100%" frameborder="0" webkitAllowFullScreen mozallowfullscreen allowFullScreen></iframe>
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		<p><i>BibTeX:</i></p>
<pre>@article{bell13opensurfaces,
	author = "Sean Bell and Paul Upchurch and Noah Snavely and Kavita Bala",
	title = "Open{S}urfaces: A Richly Annotated Catalog of Surface Appearance",
	journal = "ACM Trans. on Graphics (SIGGRAPH)",
	volume = "32",
	number = "4",
	year = "2013",
}</pre>
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<hr/>
<a id="download"></a>
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		<h3>Download the dataset</h3>
		<p>See the included README file for instructions on how to download image data.</p>
		<p><a class="btn" href="http://labelmaterial.s3.amazonaws.com/release/opensurfaces-release-0.zip"><i class="icon-download"></i> Release 0 (1.1G)</a></p>
		<br/>

		<h3>Download the full crowdsourcing platform</h3>
		<p>Our platform now includes both <a href="http://opensurfaces.cs.cornell.edu/" target="_blank">OpenSurfaces</a> and <a href="http://intrinsic.cs.cornell.edu/" target="_blank">Intrinsic Images in the Wild</a>.</p>
		<p>
			<a class="btn" href="https://www.github.com/seanbell/opensurfaces" target="_blank"><i class="icon-github"></i> Code (Github repository)</a>&nbsp;
			<a class="btn" href="/docs" target="_blank"><i class="icon-book"></i> Documentation</a>
		</p>

		<br/>
		<h3>Download the segmentation user interface</h3>
		<p>This repository contains our segmentation user interface extracted as a lightweight tool. A dummy server backend is included to run the demo.</p>
		<p>
			<a class="btn" href="https://github.com/seanbell/opensurfaces-segmentation-ui" target="_blank"><i class="icon-github"></i> Code (Github repository)</a>&nbsp;
			<a class="btn" href="http://seanbell.ca/opensurfaces-segmentation-ui/" target="_blank"><i class="icon-play-circle"></i> Online Demo</a>
		</p>
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		<a href="https://github.com/seanbell/opensurfaces-segmentation-ui" class="thumbnail" target="_blank">
			<img src="/static/img/opensurfaces-segmentation-ui.png" />
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<hr/>
<a id="acks"></a>
<h3>Acknowledgements</h3>
<p>
  This work was supported by the National Science Foundation (grants
  IIS-1161645, NSF-1011919, and NSF-1149393) and the Intel Science and
  Technology Center for Visual Computing.  We are grateful to Albert Liu and
  Keven Matzen, and to Kevin for the paper video.  We also thank our MTurk
  workers, and Patti Steinbacher in particular.  In the supplementary
  material, we acknowledge the Flickr users who released their images under
  Creative Commons licenses.  Finally, we thank the reviewers for their valuable comments.
</p>
<p>Computing resources for this project were provided by
<a href="http://aws.amazon.com/education/">AWS for Education</a>.</p>

<p>Header background pattern: courtesy of <a href="http://subtlepatterns.com/">Subtle Patterns</a>.</p>
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